PETs are technologies designed to protect personal data privacy while enabling data processing, analysis, and sharing for legitimate purposes.
Source: IQWorks Glossary — iqworks.ai | Last updated: 2026-02-01
Last verified: February 1, 2026
Privacy-Enhancing Technologies encompass a broad range of technical solutions that enable organizations to process and derive value from data while protecting individual privacy. PETs include data anonymization, pseudonymization, differential privacy, homomorphic encryption, secure multi-party computation, federated learning, and synthetic data generation.
The adoption of PETs is increasingly encouraged by regulators as a means of implementing data protection by design. ProtectIQ incorporates multiple PETs including tokenization, dynamic data masking, and anonymization to provide layered privacy protection tailored to specific use cases.
Relevant Regulations
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Related Terms
Differential Privacy
Differential privacy is a mathematical framework that adds calibrated noise to data or query results, enabling statistical analysis while providing provable privacy guarantees for individuals.
Synthetic Data
Synthetic data is artificially generated data that statistically resembles real data but contains no actual personal information, useful for testing, development, and analytics.
Data Anonymization
Anonymization irreversibly transforms personal data so that individuals can no longer be identified, even by the data controller, removing the data from privacy regulation scope.
Data Pseudonymization
Pseudonymization replaces direct identifiers with artificial identifiers, reducing privacy risk while maintaining data utility, but the data remains personal data under GDPR.